解决医学影像多模态缺失时分割不稳问题，提升关键结构识别精度。
CLoE: Expert Consistency Learning for Robust Missing Modality Segmentation
- 通过专家一致性学习控制预测一致性，增强缺失模态下的鲁棒性。
- 在BraTS 2020和MSD Prostate数据集上显著优于现有方法，尤其提升小病灶分割效果。
- 适合临床对关键结构敏感的医学图像分割场景使用。
多模态医学图像分割在推理时常面临模态缺失问题，导致不同模态专家间预测不一致，融合不稳定，尤其影响小范围前景结构的分割。本文提出专家一致性学习框架CLoE，通过决策层一致性控制实现鲁棒性建模。引入双分支专家一致性学习目标：模态专家一致性强制全局预测一致以缓解部分输入下的病例级漂移；区域专家一致性聚焦于临床关键前景区域，避免背景主导的正则化。进一步设计轻量级门控网络，将一致性得分映射为模态可靠性权重，实现融合前的特征重校准。在BraTS 2020与MSD Prostate数据集上的大量实验表明，CLoE在不完备多模态分割任务中超越现有最优方法，具备强跨数据集泛化能力，并显著提升对临床关键结构的分割鲁棒性。
原文摘要 · Abstract (English)
Multimodal medical image segmentation often faces missing modalities at inference, which induces disagreement among modality experts and makes fusion unstable, particularly on small foreground structures. We propose Consistency Learning of Experts (CLoE), a consistency-driven framework for missing-modality segmentation that preserves strong performance when all modalities are available. CLoE formulates robustness as decision-level expert consistency control and introduces a dual-branch Expert Consistency Learning objective. Modality Expert Consistency enforces global agreement among expert predictions to reduce case-wise drift under partial inputs, while Region Expert Consistency emphasizes agreement on clinically critical foreground regions to avoid background-dominated regularization. We further map consistency scores to modality reliability weights using a lightweight gating network, enabling reliability-aware feature recalibration before fusion. Extensive experiments on BraTS 2020 and MSD Prostate demonstrate that CLoE outperforms state-of-the-art methods in incomplete multimodal segmentation, while exhibiting strong cross-dataset generalization and improving robustness on clinically critical structures.
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